{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "718464bc-d3bc-4231-ae00-2a3bff2fab68",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import xgboost as xgb\n",
    "import seaborn as sns \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "np.int = int\n",
    "import matplotlib.pyplot as plt\n",
    "#导入各种用于评估模型性能和参数优化的函数和类，例如均方误差（MSE）、均绝对误差（MAE）、决定系数（R2 Score）、解释方差分（explained variance score）等\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score,explained_variance_score,mean_absolute_percentage_error,mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "#导入XGBoost库中的XGBClassifier类，用于建立XGBoost分类模型，并导入plot_importance函数用于特征重要性可视化。\n",
    "from xgboost import XGBClassifier, plot_importance\n",
    "#导入互信息函数\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "#导入lasso模型筛选特征变量\n",
    "from sklearn.linear_model import Lasso\n",
    "#导入Scikit-Optimize库中的贝叶斯优化相关的函数和类，用于超参数调优\n",
    "from skopt import gp_minimize\n",
    "from skopt.space import Real, Integer\n",
    "#导入交叉验证函数，用于评估模型性能\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#导入随机森林回归模型\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "plt.rcParams['font.family'] = 'Times New Roman'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50045870-09f1-4bd7-b236-4bafb6c90da6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df = pd.read_excel('指标.xlsx')\n",
    "d = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Feature.xlsx')\n",
    "dd = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Features.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc42cdc5-0483-4439-98af-3593081eff03",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "d=d.drop(columns=['Unnamed: 0'])\n",
    "dd=dd.drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59285029-518e-41c4-8d69-a0f2c2da0cba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>AvgSurfT_inst_chengshu</th>\n",
       "      <th>LST_Day_1km_chousui</th>\n",
       "      <th>LST_Night_1km_fennie</th>\n",
       "      <th>soil_temperature_level_2_kaihua</th>\n",
       "      <th>soil_temperature_level_2_guanjiang</th>\n",
       "      <th>soil_temperature_level_2_shouhuo</th>\n",
       "      <th>soil_temperature_level_3_chousui</th>\n",
       "      <th>soil_temperature_level_3_kaihua</th>\n",
       "      <th>soil_temperature_level_3_guanjiang</th>\n",
       "      <th>...</th>\n",
       "      <th>SVAI_chousui</th>\n",
       "      <th>SVAI_kaihua</th>\n",
       "      <th>WDRVI_bajie</th>\n",
       "      <th>WDRVI_chousui</th>\n",
       "      <th>WDVI_bajie</th>\n",
       "      <th>WDVI_yunsui</th>\n",
       "      <th>WDVI_chousui</th>\n",
       "      <th>WDVI_kaihua</th>\n",
       "      <th>wet_kaihua</th>\n",
       "      <th>亩产</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>410522</td>\n",
       "      <td>301.830072</td>\n",
       "      <td>14770.448120</td>\n",
       "      <td>13653.260039</td>\n",
       "      <td>291.679432</td>\n",
       "      <td>294.485981</td>\n",
       "      <td>301.239969</td>\n",
       "      <td>287.263466</td>\n",
       "      <td>288.780851</td>\n",
       "      <td>290.517675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.509428</td>\n",
       "      <td>0.502203</td>\n",
       "      <td>-0.270294</td>\n",
       "      <td>0.038094</td>\n",
       "      <td>0.312649</td>\n",
       "      <td>0.346212</td>\n",
       "      <td>0.380784</td>\n",
       "      <td>0.355850</td>\n",
       "      <td>-0.090609</td>\n",
       "      <td>7467.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>410421</td>\n",
       "      <td>301.881276</td>\n",
       "      <td>14811.998245</td>\n",
       "      <td>13724.830174</td>\n",
       "      <td>291.624652</td>\n",
       "      <td>294.462918</td>\n",
       "      <td>301.229017</td>\n",
       "      <td>287.616328</td>\n",
       "      <td>289.032764</td>\n",
       "      <td>290.784441</td>\n",
       "      <td>...</td>\n",
       "      <td>0.456349</td>\n",
       "      <td>0.444113</td>\n",
       "      <td>-0.220879</td>\n",
       "      <td>0.020097</td>\n",
       "      <td>0.272293</td>\n",
       "      <td>0.321097</td>\n",
       "      <td>0.313054</td>\n",
       "      <td>0.308834</td>\n",
       "      <td>-0.088144</td>\n",
       "      <td>5972.666001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>411726</td>\n",
       "      <td>298.990822</td>\n",
       "      <td>15035.873561</td>\n",
       "      <td>13760.252546</td>\n",
       "      <td>291.141219</td>\n",
       "      <td>293.187955</td>\n",
       "      <td>298.777454</td>\n",
       "      <td>287.204259</td>\n",
       "      <td>288.763693</td>\n",
       "      <td>290.271875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.375751</td>\n",
       "      <td>0.406768</td>\n",
       "      <td>-0.273682</td>\n",
       "      <td>-0.176015</td>\n",
       "      <td>0.255247</td>\n",
       "      <td>0.291260</td>\n",
       "      <td>0.276982</td>\n",
       "      <td>0.277341</td>\n",
       "      <td>-0.107184</td>\n",
       "      <td>5687.791498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>410822</td>\n",
       "      <td>302.326105</td>\n",
       "      <td>14909.855104</td>\n",
       "      <td>13684.031442</td>\n",
       "      <td>291.393057</td>\n",
       "      <td>294.128413</td>\n",
       "      <td>300.920627</td>\n",
       "      <td>286.732262</td>\n",
       "      <td>288.209019</td>\n",
       "      <td>290.136585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466787</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>-0.305916</td>\n",
       "      <td>0.009030</td>\n",
       "      <td>0.277992</td>\n",
       "      <td>0.313744</td>\n",
       "      <td>0.335973</td>\n",
       "      <td>0.327689</td>\n",
       "      <td>-0.062063</td>\n",
       "      <td>7959.597222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>411082</td>\n",
       "      <td>302.665974</td>\n",
       "      <td>14764.439462</td>\n",
       "      <td>13685.820730</td>\n",
       "      <td>292.812644</td>\n",
       "      <td>295.863324</td>\n",
       "      <td>302.456445</td>\n",
       "      <td>288.378758</td>\n",
       "      <td>289.948855</td>\n",
       "      <td>291.813767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.479437</td>\n",
       "      <td>0.456340</td>\n",
       "      <td>-0.155182</td>\n",
       "      <td>0.027083</td>\n",
       "      <td>0.329961</td>\n",
       "      <td>0.356682</td>\n",
       "      <td>0.342203</td>\n",
       "      <td>0.353976</td>\n",
       "      <td>-0.074036</td>\n",
       "      <td>7662.496889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>411081</td>\n",
       "      <td>302.379726</td>\n",
       "      <td>14864.959928</td>\n",
       "      <td>13716.550674</td>\n",
       "      <td>292.036420</td>\n",
       "      <td>294.927847</td>\n",
       "      <td>301.419777</td>\n",
       "      <td>287.740673</td>\n",
       "      <td>289.256381</td>\n",
       "      <td>291.034992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438036</td>\n",
       "      <td>0.427608</td>\n",
       "      <td>-0.222630</td>\n",
       "      <td>-0.033628</td>\n",
       "      <td>0.276777</td>\n",
       "      <td>0.307460</td>\n",
       "      <td>0.306785</td>\n",
       "      <td>0.308283</td>\n",
       "      <td>-0.090942</td>\n",
       "      <td>6613.422500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>411424</td>\n",
       "      <td>302.404313</td>\n",
       "      <td>14767.178119</td>\n",
       "      <td>13698.554350</td>\n",
       "      <td>293.142870</td>\n",
       "      <td>296.125191</td>\n",
       "      <td>302.061064</td>\n",
       "      <td>288.739608</td>\n",
       "      <td>290.273988</td>\n",
       "      <td>292.107970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537977</td>\n",
       "      <td>0.518776</td>\n",
       "      <td>-0.049587</td>\n",
       "      <td>0.143995</td>\n",
       "      <td>0.356591</td>\n",
       "      <td>0.372768</td>\n",
       "      <td>0.380640</td>\n",
       "      <td>0.392420</td>\n",
       "      <td>-0.074186</td>\n",
       "      <td>7579.469809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>411724</td>\n",
       "      <td>300.297286</td>\n",
       "      <td>14800.494981</td>\n",
       "      <td>13753.180467</td>\n",
       "      <td>291.750152</td>\n",
       "      <td>293.718662</td>\n",
       "      <td>299.219180</td>\n",
       "      <td>287.779530</td>\n",
       "      <td>289.413163</td>\n",
       "      <td>290.932713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.511967</td>\n",
       "      <td>0.563379</td>\n",
       "      <td>0.082507</td>\n",
       "      <td>0.149694</td>\n",
       "      <td>0.389395</td>\n",
       "      <td>0.347135</td>\n",
       "      <td>0.345173</td>\n",
       "      <td>0.381115</td>\n",
       "      <td>-0.079914</td>\n",
       "      <td>5754.140236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>411324</td>\n",
       "      <td>300.250438</td>\n",
       "      <td>14870.302144</td>\n",
       "      <td>13755.946153</td>\n",
       "      <td>291.086794</td>\n",
       "      <td>293.369445</td>\n",
       "      <td>300.014684</td>\n",
       "      <td>287.141116</td>\n",
       "      <td>288.584741</td>\n",
       "      <td>290.356689</td>\n",
       "      <td>...</td>\n",
       "      <td>0.480808</td>\n",
       "      <td>0.460888</td>\n",
       "      <td>-0.102704</td>\n",
       "      <td>0.005744</td>\n",
       "      <td>0.309025</td>\n",
       "      <td>0.317261</td>\n",
       "      <td>0.348503</td>\n",
       "      <td>0.307512</td>\n",
       "      <td>-0.097445</td>\n",
       "      <td>5467.128359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>410122</td>\n",
       "      <td>302.101448</td>\n",
       "      <td>14900.316284</td>\n",
       "      <td>13728.293344</td>\n",
       "      <td>292.494994</td>\n",
       "      <td>295.405081</td>\n",
       "      <td>301.660649</td>\n",
       "      <td>288.002027</td>\n",
       "      <td>289.590761</td>\n",
       "      <td>291.427115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.380461</td>\n",
       "      <td>0.390789</td>\n",
       "      <td>-0.356993</td>\n",
       "      <td>-0.172850</td>\n",
       "      <td>0.260427</td>\n",
       "      <td>0.290878</td>\n",
       "      <td>0.283795</td>\n",
       "      <td>0.284526</td>\n",
       "      <td>-0.095178</td>\n",
       "      <td>6637.200003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  AvgSurfT_inst_chengshu  LST_Day_1km_chousui   \n",
       "0    410522              301.830072         14770.448120  \\\n",
       "1    410421              301.881276         14811.998245   \n",
       "2    411726              298.990822         15035.873561   \n",
       "3    410822              302.326105         14909.855104   \n",
       "4    411082              302.665974         14764.439462   \n",
       "..      ...                     ...                  ...   \n",
       "97   411081              302.379726         14864.959928   \n",
       "98   411424              302.404313         14767.178119   \n",
       "99   411724              300.297286         14800.494981   \n",
       "100  411324              300.250438         14870.302144   \n",
       "101  410122              302.101448         14900.316284   \n",
       "\n",
       "     LST_Night_1km_fennie  soil_temperature_level_2_kaihua   \n",
       "0            13653.260039                       291.679432  \\\n",
       "1            13724.830174                       291.624652   \n",
       "2            13760.252546                       291.141219   \n",
       "3            13684.031442                       291.393057   \n",
       "4            13685.820730                       292.812644   \n",
       "..                    ...                              ...   \n",
       "97           13716.550674                       292.036420   \n",
       "98           13698.554350                       293.142870   \n",
       "99           13753.180467                       291.750152   \n",
       "100          13755.946153                       291.086794   \n",
       "101          13728.293344                       292.494994   \n",
       "\n",
       "     soil_temperature_level_2_guanjiang  soil_temperature_level_2_shouhuo   \n",
       "0                            294.485981                        301.239969  \\\n",
       "1                            294.462918                        301.229017   \n",
       "2                            293.187955                        298.777454   \n",
       "3                            294.128413                        300.920627   \n",
       "4                            295.863324                        302.456445   \n",
       "..                                  ...                               ...   \n",
       "97                           294.927847                        301.419777   \n",
       "98                           296.125191                        302.061064   \n",
       "99                           293.718662                        299.219180   \n",
       "100                          293.369445                        300.014684   \n",
       "101                          295.405081                        301.660649   \n",
       "\n",
       "     soil_temperature_level_3_chousui  soil_temperature_level_3_kaihua   \n",
       "0                          287.263466                       288.780851  \\\n",
       "1                          287.616328                       289.032764   \n",
       "2                          287.204259                       288.763693   \n",
       "3                          286.732262                       288.209019   \n",
       "4                          288.378758                       289.948855   \n",
       "..                                ...                              ...   \n",
       "97                         287.740673                       289.256381   \n",
       "98                         288.739608                       290.273988   \n",
       "99                         287.779530                       289.413163   \n",
       "100                        287.141116                       288.584741   \n",
       "101                        288.002027                       289.590761   \n",
       "\n",
       "     soil_temperature_level_3_guanjiang  ...  SVAI_chousui  SVAI_kaihua   \n",
       "0                            290.517675  ...      0.509428     0.502203  \\\n",
       "1                            290.784441  ...      0.456349     0.444113   \n",
       "2                            290.271875  ...      0.375751     0.406768   \n",
       "3                            290.136585  ...      0.466787     0.392344   \n",
       "4                            291.813767  ...      0.479437     0.456340   \n",
       "..                                  ...  ...           ...          ...   \n",
       "97                           291.034992  ...      0.438036     0.427608   \n",
       "98                           292.107970  ...      0.537977     0.518776   \n",
       "99                           290.932713  ...      0.511967     0.563379   \n",
       "100                          290.356689  ...      0.480808     0.460888   \n",
       "101                          291.427115  ...      0.380461     0.390789   \n",
       "\n",
       "     WDRVI_bajie  WDRVI_chousui  WDVI_bajie  WDVI_yunsui  WDVI_chousui   \n",
       "0      -0.270294       0.038094    0.312649     0.346212      0.380784  \\\n",
       "1      -0.220879       0.020097    0.272293     0.321097      0.313054   \n",
       "2      -0.273682      -0.176015    0.255247     0.291260      0.276982   \n",
       "3      -0.305916       0.009030    0.277992     0.313744      0.335973   \n",
       "4      -0.155182       0.027083    0.329961     0.356682      0.342203   \n",
       "..           ...            ...         ...          ...           ...   \n",
       "97     -0.222630      -0.033628    0.276777     0.307460      0.306785   \n",
       "98     -0.049587       0.143995    0.356591     0.372768      0.380640   \n",
       "99      0.082507       0.149694    0.389395     0.347135      0.345173   \n",
       "100    -0.102704       0.005744    0.309025     0.317261      0.348503   \n",
       "101    -0.356993      -0.172850    0.260427     0.290878      0.283795   \n",
       "\n",
       "     WDVI_kaihua  wet_kaihua           亩产  \n",
       "0       0.355850   -0.090609  7467.000138  \n",
       "1       0.308834   -0.088144  5972.666001  \n",
       "2       0.277341   -0.107184  5687.791498  \n",
       "3       0.327689   -0.062063  7959.597222  \n",
       "4       0.353976   -0.074036  7662.496889  \n",
       "..           ...         ...          ...  \n",
       "97      0.308283   -0.090942  6613.422500  \n",
       "98      0.392420   -0.074186  7579.469809  \n",
       "99      0.381115   -0.079914  5754.140236  \n",
       "100     0.307512   -0.097445  5467.128359  \n",
       "101     0.284526   -0.095178  6637.200003  \n",
       "\n",
       "[102 rows x 66 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75eb75d7-b0ce-4b35-98b5-91a2ab2470aa",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb20839c-6340-44b1-a9e7-3c0c367273c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############删除缺失率高于05的特征\n",
    "null_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        null_list.append([col,d[col].isnull().sum() / d.shape[0]])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "null_df = pd.DataFrame(null_list,columns = ['特征名称','缺失率'])\n",
    "del_cols = null_df['特征名称'][null_df['缺失率'] >= 0.5]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aba9ae28-97f8-4fec-bc66-dd5fbfd318ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############表格中用绿色标记表示删除同值比例高的特征\n",
    "tz_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        tz_list.append([col,d[d[col] == d[col].mode()[0]].shape[0] / (d.shape[0] - d[col].isnull().sum())])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "tz_df = pd.DataFrame(tz_list,columns = ['特征名称','同值比例'])\n",
    "del_cols = tz_df['特征名称'][tz_df['同值比例'] >= 0.9]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4032c90c-2e9f-47ff-9b43-383d7419a1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "d=d.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','NAME','soil_temperature_level_2_shouhuo',\n",
    "                  'VDVI_shouhuo','AvgSurfT_inst_chengshu','soil_temperature_level_2_guanjiang', 'soil_temperature_level_3_guanjiang', \n",
    "                  'soil_temperature_level_1_guanjiang', 'SoilMoi0_10cm_inst_guanjiang', 'SoilMoi10_40cm_inst_guanjiang'])\n",
    "dd=dd.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','NAME','soil_temperature_level_2_shouhuo',\n",
    "                  'VDVI_shouhuo','AvgSurfT_inst_chengshu','soil_temperature_level_2_guanjiang', 'soil_temperature_level_3_guanjiang', \n",
    "                  'soil_temperature_level_1_guanjiang', 'SoilMoi0_10cm_inst_guanjiang', 'SoilMoi10_40cm_inst_guanjiang'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a117973-8634-4679-8251-f818d42ab8c0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#d=d.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])\n",
    "#dd=dd.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299d2aaa-7024-4510-a82e-7a8c7e2bce87",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 全部入模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0fc106ad-358a-451e-bbdf-57729747d20a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train =d.drop(columns = [\"亩产\"])\n",
    "y_train = d['亩产']\n",
    "x_test =dd.drop(columns = [\"亩产\"])\n",
    "y_test = dd['亩产']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef9e4a27-8669-49ea-b699-edba291bb268",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = xgb.XGBRegressor(n_estimators=150,\n",
    "                                 max_depth=5,\n",
    "                                 learning_rate=0.1,\n",
    "                                 \n",
    "                                 random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44180510-e0eb-449d-bc96-8b985e0ca0c3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgboost_model_fit = model.fit(x_train, y_train)\n",
    "y_pred_bo = xgboost_model_fit.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "136d8f93-7a6c-4694-bbb0-cbc45073abd5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>平均绝对误差（MAPE）</th>\n",
       "      <th>平均绝对误差（MAE）</th>\n",
       "      <th>均方根误差（RMSE）</th>\n",
       "      <th>决定系数（R^2）</th>\n",
       "      <th>解释方差</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>0.098677</td>\n",
       "      <td>571.835848</td>\n",
       "      <td>717.133763</td>\n",
       "      <td>0.714564</td>\n",
       "      <td>0.714888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         平均绝对误差（MAPE）  平均绝对误差（MAE）  均方根误差（RMSE）  决定系数（R^2）      解释方差\n",
       "xgboost      0.098677   571.835848   717.133763   0.714564  0.714888"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 平均绝对百分比误差\n",
    "mape=mean_absolute_percentage_error(y_test,y_pred_bo)\n",
    "# 计算均方根误差（RMSE）\n",
    "rmse = mean_squared_error(y_test, y_pred_bo, squared=False)\n",
    "# 计算平均绝对误差（MAE）\n",
    "mae = mean_absolute_error(y_test, y_pred_bo)\n",
    "# 计算决定系数（R^2）\n",
    "r2 = r2_score(y_test, y_pred_bo)\n",
    "# 计算解释方差分\n",
    "explained_var = explained_variance_score(y_test, y_pred_bo)\n",
    "\n",
    "results1 = pd.DataFrame()\n",
    "results1['平均绝对误差（MAPE）'] = [mape]\n",
    "results1['平均绝对误差（MAE）'] = [mae]\n",
    "results1['均方根误差（RMSE）'] = [rmse]\n",
    "results1['决定系数（R^2）'] = [r2]\n",
    "results1['解释方差'] = [explained_var]\n",
    "results1.index = ['xgboost']\n",
    "results1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "43a37742-b0a1-4ac8-9f09-39d1fe7992bf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>precent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7467.000138</td>\n",
       "      <td>7086.412598</td>\n",
       "      <td>-5.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5972.666001</td>\n",
       "      <td>6819.524414</td>\n",
       "      <td>14.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5687.791498</td>\n",
       "      <td>5030.607422</td>\n",
       "      <td>-11.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7959.597222</td>\n",
       "      <td>7539.835938</td>\n",
       "      <td>-5.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7662.496889</td>\n",
       "      <td>6916.489746</td>\n",
       "      <td>-9.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6613.422500</td>\n",
       "      <td>7707.619629</td>\n",
       "      <td>16.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7579.469809</td>\n",
       "      <td>7153.873047</td>\n",
       "      <td>-5.62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5754.140236</td>\n",
       "      <td>6500.386230</td>\n",
       "      <td>12.97%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5467.128359</td>\n",
       "      <td>5905.918945</td>\n",
       "      <td>8.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6637.200003</td>\n",
       "      <td>6074.206543</td>\n",
       "      <td>-8.48%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y_test       y_pred  precent\n",
       "0    7467.000138  7086.412598   -5.10%\n",
       "1    5972.666001  6819.524414   14.18%\n",
       "2    5687.791498  5030.607422  -11.55%\n",
       "3    7959.597222  7539.835938   -5.27%\n",
       "4    7662.496889  6916.489746   -9.74%\n",
       "..           ...          ...      ...\n",
       "97   6613.422500  7707.619629   16.55%\n",
       "98   7579.469809  7153.873047   -5.62%\n",
       "99   5754.140236  6500.386230   12.97%\n",
       "100  5467.128359  5905.918945    8.03%\n",
       "101  6637.200003  6074.206543   -8.48%\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_array = np.array(y_test)\n",
    "\n",
    "y_pred_series = pd.Series(y_pred_bo, name='y_pred')\n",
    "y_test_series = pd.Series(y_test_array, name='y_test')\n",
    "dftest= pd.concat([y_test_series, y_pred_series], axis=1)\n",
    "dftest['precent'] = ((dftest['y_pred'] - dftest['y_test']) / dftest['y_test']) * 100\n",
    "dftest['precent'] = dftest['precent'].apply(lambda x: '{:.2f}%'.format(x))\n",
    "dftest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b1994e2-af3b-478a-992d-e4128e188f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_sorted = np.sort(y_test)\n",
    "y_pred_sorted = y_pred_bo[np.argsort(y_test)]\n",
    "\n",
    "# 绘制折线对比图\n",
    "plt.plot(range(len(y_test_sorted)), y_test_sorted, label='Actual')\n",
    "plt.plot(range(len(y_pred_sorted)), y_pred_sorted, label='Predicted',linestyle='--')\n",
    "#plt.xlabel('Index')\n",
    "plt.ylabel('Value',fontsize=16)\n",
    "plt.title('Actual vs. Predicted (Bo-XGBoost)',fontsize=16)\n",
    "plt.legend()\n",
    "plt.ylim(1000, 10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df4a379a-10bb-4f49-8b5f-1490cbda5d25",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(y_test, y_pred_bo, alpha=0.5, label='Data Points')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('bh-XGBoost Scatter Plot of Actual vs. Predicted Values')\n",
    "\n",
    "# 添加对角线\n",
    "max_value = max(max(y_test), max(y_pred_bo))\n",
    "min_value = min(min(y_test), min(y_pred_bo))\n",
    "plt.plot([min_value, max_value], [min_value, max_value], color='red', linestyle='--', linewidth=2, label='Perfect Prediction')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3896133d-f939-4563-8d25-5aed0e0b5413",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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